A reliable tool for evaluating brain health

ABSTRACT

Systems and a computer implemented method for classifying a brain status of a subject, from a neural activity response of the subject to an induced TMS stimulation; the method comprising: constructing a machine learning classifier (MLC) configured to classify a subjects brain status; training the MLC using a training set, the training set comprising pairs of training output-classification vectors and their corresponding training input vectors, all extracted from a database of subjects with known brain status classifications; and applying the trained MLC on an input vector comprising features extracted from a tested-subjects brain neural activity response to the induced TMS stimulation, to obtain an output classification vector for the tested-subjects brain status.

BACKGROUND OF THE INVENTION

The ability to deal with a challenge is limited by the tools one has todiagnose, evaluate and monitor the impact of the challenge. In theneuropsychiatric world, the ability to deal with the expanding risk ofage associated brain disorders, such as Alzheimer's disease (AD), andother neurodegenerative—psychiatric disorders, an impact of which cannotbe overstated, is limited by the lack of any tools which enable theevaluation and monitoring of brain health status. Current availabletechnologies such as magnetic resonance imaging (MRI) or computedtomography (CT) scans provide high resolution images of the structuraltopology of the network but lack the ability to directly monitor brainfunctionality. Advanced technologies like, functional MRI (fMRI), orpositron emission tomography (PET-CT) use indirect measurement as bloodflow which correlates highly with regional changes in the level ofactivity but cannot be used for the direct evaluation of the logicaltopology of the network (Gore et al, J Clin Invest. 2003 July; Simon Ret al, Seminars in Nuclear Medicine, September 2009). Moreover, thesetools do not provide any valuable insights when evaluating brain healthduring normal aging or age-related pathological deterioration, such asmild cognitive impairment or early dementia, paradoxically, leaving themost prevalent brain related deterioration to be diagnosed and monitoredby a subjective physician assessment, or cognitive evaluation at best.

The world of clinical neurology and psychiatry have some robusttreatment tools such as brain directed pharmacological agents, forexample Lithium (Grant B et al., Paediatr Drugs. 2018 August) andKetamine (Wray N H et al, Mol Psychiatry. 2018 June) or electroconvulsive treatment (ECT, Veltman E M et al, J ECT. 2018 May). Still,most conditions do not have any specific proven treatment. Moreover, theactual effect of many brain directed drugs, (antidepressants forexample), remains a matter of controversy (Salanti G et al, Int JEpidemiol 2018) due to the lack of any reliable, direct and objectivetools for diagnosis, evaluation, and monitoring of brain functionalstatus.

Electrophysiology is a well-established and important for evaluatingbrain network functionality. The use of electrophysiologicalmeasurements to characterize and monitor brain network activity has beenused extensively over the last seven decades. From Hebb's pioneeringwork describing brain organization and synaptic plasticity (Hebb, D. O.(1949). The Organization of Behavior. New York: Wiley & Sons). Bliss andLomo's work describing long term potentiation (LTP) (Lomo T, 2003 AprilPhilos Trans R Soc Lond B Biol Sci), the breakthrough discoveries ofWiesel et al. on brain plasticity in the visual system (Wiesel, T. N. etal. (1963) J. Neurophysiol) and other discoveries lead the significantadvancements in world of neuroscience research.

However, despite the understanding that electrophysiology is animportant tool for understanding brain functionality in health anddisease, there is still a need for a tool that enables an easy and safeelectrophysiological measurement of brain network functionality, in theclinical practice.

Electrophysiological measurements can be generally divided into twogroups of parameters: network integrity (meaning its connectivity andcoherence), and network plasticity. The functional network connectivitydepends on the synchronous activation of neurons and is used todetermine the functional network integrity. Network coherence refers tothe level of synchrony between two or more brain regions and is used todetermine the strength of connectivity between specific brain regions(Bowyer et al. Neuropsychiatric Electrophysiology, 2016).Neuroplasticity, or brain plasticity, is an ability of the brain, tocontinuously adapt its functional and structural organization tochanging requirements (Baroncelli et al. 2011, Neuronal plasticity).Neuronal plasticity allows the brain to reorganize neuronal networks inresponse to environmental stimulation, to remember information and torecover from brain and spinal cord injuries (Johnston, DevelopmentalDisabilities Research Reviews, June 2009). Neuronal plasticity isessential to the establishment and maintenance of brain circuitry.Therefore, in order to enable a personalized clinical evaluation ofbrain health, there is a need for a reliable, reproducible and adaptableelectrophysiological method to assess the functional network integrityand plasticity across each individual's lifespan.

Transcranial magnetic stimulation (TMS) is a non-invasive brainstimulation method that allows the study of human cortical function invivo (Corthout et al. Experimental Brain Research, 2001 (Hallett, 2007;Rossini et al., 2015). Using TMS for examining human corticalfunctionality is enhanced by combining TMS with simultaneousregistration of an electroencephalograph (EEG). EEG provides anopportunity to directly measure the cerebral response to TMS, measuringthe cortical TMS evoked potential (TEP), and is used to assess cerebralreactivity across wide areas of neocortex (Casula et al. NeuroImage,September 2014). Studies integrating TMS with EEG (TMS-EEG) have shownthat TMS produces waves of activity that reverberate throughout thecortex and that are reproducible and reliable (Casarotto et al., 2010;Lioumis et al., 2009), thus providing direct information about corticalexcitability and connectivity with excellent time resolution (Thut etal. Brain Topography, 2010; Ilmoniemi, Brain topography, 2010). Byevaluating the propagation of evoked activity in different behaviouralstates and in different tasks, TMS-EEG has been used to causally probethe dynamic effective connectivity of human brain networks (Kugiumtzisand Kimiskidis, 2015; Shafi et al., 2012).

An important feature of TEP topography is that even though only onecortical hemisphere is stimulated, bi-hemispheric EEG responses areevoked with different features. TMS-evoked activity propagates from thestimulation site ipsilaterally via association fibers, contralaterallyvia transcallosal fibers, and to subcortical structures via projectionfibers. A single TMS pulse delivered over the primary motor cortex (M1)results in a sequence of positive and negative EEG peaks at specificlatencies (i.e., N45, P60, N100, and P180; namely negative peaks at 45mSec (N45) and 100 mSec (N100) after stimulation, positive peaks at 60mSec (P60) and 180 mSec (P180) after stimulation).

This pattern of response indicates synaptic activity, specifically theGlutamate-excitatory and Gamma-aminobutyric acid (GABA)-inhibitorytransmission balance (Du X, Brain Stimul. 2018 September). It isbelieved that the N45 peak represents activity of α1-subunit-containingGABA-A receptors, whereas the N100 represents activity of GABA-Breceptors (Premoli, Journal of Neuroscience 16 Apr. 2014). TheseTMS-evoked cortical potentials last for up to 300 mSec in both thevicinity of the stimulation, as well as in remote interconnected brainareas that reflect long term changes in cortical networkexcitation-inhibition balance, referred to as brain network plasticity(Ilmoniemi et al., 1997; Bonato et al., 2006; Lioumis et al., 2009;Premoli et al.).

Changes in this TMS evoked plasticity measurements provide importantinsights into cortical processing both in health (Massimini et al.,2005; Ferrarelli et al., 2010) and disease (Rosanova et al., Neuronalnetwork analysis: concepts and experimental approaches. Totowa, N.J.:Humana Press; 2012. p. 435e57; Ragazzoni et al., 2013) such as majordepression and schizophrenia (Farzan et al.; Radhu et al, 2015). Thus,developing a bed side tool, based on the combinations of TMS and EEGtechnologies, has the potential to monitor pathological changes andtherapy-induced modifications in cortical circuits (Premoli et al.).

SUMMARY OF THE INVENTION

According to some embodiments of the invention a new computerimplemented method is provided configured for classifying a brain statusof a subject, from a neural activity response of the subject to aninduced TMS stimulation; the method comprising:

-   -   constructing a machine learning classifier (MLC) configured to        classify a subject's brain status;    -   training the MLC using a training set, the training set        comprising pairs of training output-classification vectors and        their corresponding training input vectors, all extracted from a        database of subjects with known brain status classifications,        wherein:        -   each training output-classification vector is determined            based on at least one of a database-subject's known: MRI            readings, physician/s classification, cognitive testis            evaluation, and any combination thereof;        -   each training input vector comprises features extracted from            a database-subject's brain neural activity response to the            induced TMS stimulation;    -   applying the trained MLC on an input vector comprising features        extracted from a tested-subject's brain neural activity response        to the induced TMS stimulation, to obtain an output        classification vector for the tested-subject's brain status.

According to some embodiments, each output classification vector andaccordingly each training output classification vector comprise featuresselected from:

-   -   physical status selected from: healthy/not healthy, the brain's        evaluated age, neurological disorders, neurodegetative disease,        Alzheimer, Dementia, small vessels disease, Psychiatric        disorders, depression, chronic pain, physical injury,        pathophysiological abnormalities, structural damage of the grey        matter, structural damage of the white matter, functional        damage, internal bleeding, balance between excitation and        inhibition in the regional cortical network, intra-cranial        pressure, Cerebrovascular Accident (CVA), Basal ganglia injury,        brain stem injury, corticospinal track injury, frontal lobe        injury, temporal lobe injury, any combination thereof;    -   brain MRI-T1—gray matter and white matter volume and/or surface        of cortical and subcortical areas;    -   diffused tensor MRI imaging (MRI-DWI)—white matter measures of        fractional anisotropy (FA) and mean diffusivity (MD); and    -   and any combination thereof.

According to some embodiments, the method further comprising a step ofdetermining each training output-classification vector, based on atleast one of a database-subject's known features.

According to some embodiments, the MLC comprises at least one moduleselected from:

-   -   a multi layered MLC;    -   a classification module, configured for separation of the        extracted features into discrete classification groups, selected        from:        -   support vector machine (SVM),        -   decision trees, and        -   K-nearest neighbors;    -   a registration module configured for continuous data prediction,        selected from:        -   linear and/or non-linear regression,        -   artificial neural network (NN), and        -   adaptive fuzzy logic learning; and    -   any combination thereof.

According to some embodiments, each of the input vectors and accordinglyeach of the training input vectors further comprises at least onefeature selected from: age, gender, known medical status, drugtreatment, blood pressure, and any combination thereof.

According to some embodiments, the TMS simulation frequency is selectedfrom:

-   -   below 0.5 Hz, for a neural response, which does not depend upon        stimulations history; and    -   above 0.5 Hz, such that a neural response to pulses are affected        by the previously provided pulses, thereby indicating short term        plasticity.

According to some embodiments, the method further comprising steps of:

-   -   receiving, via an EEG device, a neural activity response of a        subject's brain to the induced TMS stimulation to one or more        predetermined brain regions of a subject; and    -   extracting response features from the subject's neural activity        response, as elements for an input vectors or a training input        vector.

According to some embodiments, the step of extracting is at least basedon positive and negative peaks at the neural activity response, andwherein the response features comprise at least one of the response's:

-   -   signal amplitudes;    -   amplitude latencies;    -   principle component analysis (PCA) and/or independent component        analysis (ICA);    -   slopes between positive and negative peaks;    -   charge transfer;    -   lag of signal phase from healthy signal;    -   signal correlation to a healthy subject signal model;    -   ratio between segments in the signal of the same sensor, when        TMS induced frequency is above 0.5 Hz;    -   coherence between the signal of different sensors;    -   brain connectivity; and    -   any combination thereof.

According to some embodiments, the step of extracting comprisesdetermining the positive and negative peaks, at time steps selected froma group consisting of: about 60 mSec, about 100 mSec, about 180 mSec andany combination thereof.

According to some embodiments, the step of extracting comprisesdetermining the positive and negative peaks, at time steps selected froma group consisting of: about 45 mSec, about 120 mSec, about 180 mSec,300 mSec and any combination thereof.

According to some embodiments, the extracted slopes are provided betweendetermined positive and negative signal peaks, which are adjacent,thereby extracting peaks' relation. According to some embodiments, theextracted slopes are provided between determined peaks, which may not beadjacent.

According to some embodiments, the step of extracting features furthercomprises comparing the slope of the 60 mSec peak with the 100 mSec peak(60-100 slope), versus the slope of 100 mSec peak with 180 mSec peak(100-180 slope).

According to some embodiments, the step of extracting features furthercomprises comparing the slope of the 45 mSec peak with the 120 mSec peak(45-120 slope), versus the slope of 180 mSec peak with 300 mSec peak(180-300 slope).

According to some embodiments, the TMS is induced in several sequentialstimulations, each at different intensity.

According to some embodiments, the TMS stimulated brain region isselected from a group consisting: frontal, parietal, temporal, occipital(right and left hemispheres) and any combination thereof.

According to some embodiments, a new apparatus is provided configured toevaluate brain state of a subject, the apparatus comprising:

-   -   a directed inspective/diagnostic stimulation unit, configured to        induce TMS diagnostic stimulation to a predetermined brain        region of the subject;    -   a brain activity EEG sensor, configured to measure a neural        activity response to the diagnostic stimulation induced by the        directed brain stimulation unit; and    -   a processing circuitry and at least one memory unit, in wired or        wireless communication with the brain activity sensor, the        processing circuitry is configured to execute the method steps        of any one of the preceding method steps for classifying a brain        status of a subject, from a neural activity response of the        subject to an induced TMS stimulation.

According to some embodiments, a new transient and/or non-transientcomputer readable medium (CRM) is provided that, when loaded into amemory of a computing device and executed by at least one processor ofthe computing device, configured to execute the steps of the computerimplemented method according to any one of the preceding method stepsfor classifying a brain status of a subject, from a neural activityresponse of the subject to an induced TMS stimulation.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter regarded as the invention is particularly pointed outand distinctly claimed in the concluding portion of the specification.The invention, however, both as to organization and method of operation,together with objects, features, and advantages thereof, may best beunderstood by reference to the following detailed description when readwith the accompanying drawings in which:

FIG. 1 schematically demonstrates modules of the DELPhi system,according to some embodiments of the invention;

FIG. 2 schematically demonstrates a flow chart for extracting at leastpart of the input features to a machine learning classifier (MLC),according to some embodiments of the invention;

FIG. 3 schematically demonstrates a computer implemented method,configured for classifying a brain status of a subject, from a neuralactivity response of the subject to an induced TMS stimulation;

FIG. 4 schematically demonstrates an example for an MLC comprising amulti layered neural network (NN), according to some embodiments of theinvention;

FIGS. 5A and 5B schematically demonstrate MLC method phases: Training(FIG. 5A) and Application (FIG. 5B), according to some embodiments ofthe invention;

FIGS. 6A, 6B and 6C schematically demonstrate examples for differenttraining phases, according to some embodiments of the invention;

FIGS. 7A and 7B schematically demonstrate an example for the Applicationphase of integrated MLC modules (FIG. 7A) and their method steps (FIG.7B), according to some embodiments of the invention;

FIGS. 8A, 8B, 8C, 8D and 8E demonstrate intensity dependent TMS evokedresponse, in young healthy subjects (FIGS. 8B-8E), according to someembodiments of the invention;

FIGS. 9A, 9B, 9C and 9D demonstrate frequency dependent TMS evokedresponse in young healthy subjects, according to some embodiments of theinvention;

FIGS. 10A, 10B, 10C, 10D and 10E demonstrate Sham Controlled TMS evokedstimulation in healthy subjects, according to some embodiments of theinvention;

FIGS. 11A, 11B, 11C, 11D and 11E demonstrate representative test vs.re-test of TMS evoked response, according to some embodiments of theinvention;

FIGS. 12A, 12B, 12C, 12D and 12E demonstrate representative test vs.re-test of TMS evoked response according to Bland-Altman plot, accordingto some embodiments of the invention;

FIGS. 13A, 13B, 13C and 13D demonstrate representative N100-P180 slopevs. P60-N100 slope, according to some embodiments of the invention;

FIGS. 14A, 14B and 14C demonstrate representative measurement of EEGsignal [μv] in time [mSec]; FIG. 14A for healthy subject and it'saverage 14B; FIG. 14C for abnormal subject, according to someembodiments of the invention;

FIGS. 15A, 15B, 15C and 15D demonstrate representative short termplasticity of charge transfer as a function of short term plasticity ofN100-P180 Slope, according to some embodiments of the invention;

FIGS. 16A and 16B demonstrate connectivity differences between healthyintact brain and specific brain injuries in left side, according to someembodiments of the invention;

FIGS. 17A and 17B demonstrate connectivity differences between healthyintact brain and specific brain injuries in midbrain and brain stem,according to some embodiments of the invention;

FIGS. 18A and 18B demonstrate connectivity differences between healthyintact brain and specific brain injuries at the right side, according tosome embodiments of the invention;

FIGS. 19A and 19B demonstrate short term plasticity differences betweenhealthy intact brain and specific brain injuries at the left side,according to some embodiments of the invention;

FIGS. 20A and 20B demonstrate short term plasticity differences betweenhealthy intact brain and specific brain injuries in midbrain and brainstem, according to some embodiments of the invention;

FIGS. 21A and 21B demonstrate short term plasticity differences betweenhealthy intact brain and specific brain injuries in right side,according to some embodiments of the invention;

FIGS. 22A, 22B, 22C and 22D demonstrate evaluation of age dependentchanges, and brain health changes in connectivity maps and networkstrength, according to some embodiments of the invention;

FIGS. 23A, 23B, 23C, 23D, 23E, and 23F demonstrate age dependentchanges, and brain health change in brain network functionality andconnectivity feature of early 60-100 slope and late 100-180 slope,according to some embodiments of the invention;

FIGS. 24A, 24B, 24C, 24D, 24E and 24F demonstrate age dependent changesand brain health change in network short term plasticity, according tosome embodiments of the invention;

FIG. 25 demonstrate correlation between brain atrophy of grey matter asmeasured by MRI in healthy subjects with parameters of network shortterm plasticity and connectivity, according to some embodiments of theinvention; and

FIG. 26 demonstrate prediction model for brain atrophy of grey matter asmeasured by MRI in healthy subjects by parameters of network short termplasticity and connectivity, according to some embodiments of theinvention.

It will be appreciated that for simplicity and clarity of illustration,elements shown in the figures have not necessarily been drawn to scale.For example, the dimensions of some of the elements may be exaggeratedrelative to other elements for clarity. Further, where consideredappropriate, reference numerals may be repeated among the figures toindicate corresponding or analogous elements.

DETAILED DESCRIPTION OF THE PRESENT INVENTION

In the following detailed description, numerous specific details are setforth in order to provide a thorough understanding of the invention.However, it will be understood by those skilled in the art that thepresent invention may be practiced without these specific details. Inother instances, well-known methods, procedures, and components have notbeen described in detail so as not to obscure the present invention.

The term “slope” as used herein, refers to the linear coefficient “a”,where a x+b is the linear equation. According to some embodiments, thelinear equation is determined by connecting two dots (two measurements).Alternatively, according to some embodiments, the linear equation isdetermined by linear regression of plurality of dots (plurality ofmeasurements).

As used herein, in one embodiment the term “about” refers to ±10%. Inanother embodiment, the term “about” refers to ±9%. In anotherembodiment, the term “about” refers to ±8%. In another embodiment, theterm “about” refers to ±7%. In another embodiment, the term “about”refers to ±6%. In another embodiment, the term “about” refers to ±5%. Inanother embodiment, the term “about” refers to ±4%. In anotherembodiment, the term “about” refers to ±3%. In another embodiment, theterm “about” refers to ±2%. In another embodiment, the term “about”refers to ±1%.

The term “balance” as used herein, refers according to some embodiments,to the ability to maintain the ratio between excitation and inhibition(balance). Balance is an important physiological characteristic of brainplasticity.

The term “hotspot” as used herein, refers to a selected group of sensorsthat together represent a specific brain area.

The currently provided invention deals with a DirectElectro-Physiological Imaging (DELPhi) technology. DELPhi combinesTMS-EEG and their robust scientific infrastructure into one completeautomated acquisition and analysis system, making the criticalneuro-physiological biomarkers clinically accessible.

The evidence described in the following supports DELPhi as the firstclinically available tool for the successful evaluation, monitoring anddiagnosis of age dependent brain conditions and disorders in anon-invasive, safe, easy to use and cost-effective manner.

Reference is now made to FIG. 1, which demonstrates the DELPHI systemarchitecture 100. The system comprises customized integrated hardwaredevice 200, comprising: TMS unit 202, an EEG unit 203 and a computer 201comprising at least one processor. The hardware unit 200 is configuredfor an automated data acquisition and for analysis via its software 300.According to some embodiments, the hardware section 200 of the DELPhisystem for evaluating brain state of a subject comprises:

-   -   a directed inspective/diagnostic stimulation unit 202,        configured to induce TMS diagnostic stimulation to a        predetermined brain region of the subject;    -   a brain activity EEG sensor 203, configured to measure a neural        activity response to the diagnostic stimulation induced by the        directed brain stimulation unit; and    -   a processing circuitry 201 in wired or wireless communication        with the brain activity sensor, the processing circuitry is        configured to evaluate a neuroplasticity and/or excitability of        neural-structures in the predetermined brain region based on the        neural activity response to the diagnostic stimulation;

as also demonstrated in WO2016/016888 (specifically in FIG. 1).

According to some embodiments, the inspective/diagnostic stimulationunit comprises a magnetically induced stimulation device and acontroller thereof, configured to provide transcranial magneticstimulation (TMS) pulses to the region of the brain of the subject.According to some embodiments, the intensity of the provided TMS pulsesis within a range (FIG. 8A, 412), in which the subject's brain neuralactivity response is reactive to a change of the TMS pulse intensity.FIG. 8A schematically illustrates a stimulation intensity operationalrange 402, according to some embodiments. As illustrated, the EEGresponse to low stimulation intensities is relatively unchanged 411,until the stimulation intensity surpasses a certain value (lowerthreshold), then the EEG response reacts/increases as the stimulationintensity increases 412, until the stimulation intensity reaches anothervalue (upper threshold) in which the EEG response no longer responds toincreases in the stimulation intensity 413.

The currently provided evaluation of neuro-physiological properties, asnetwork strength and plasticity, enables the characterization of agedependent brain functional changes. Moreover, the DELPhi acquisition andanalysis system provide clinicians with a noninvasive, direct andobjective tool for evaluating and monitoring brain health throughoutaging, and enable an early detection of abnormal physiological changesleading to neurodegeneration, as for the case of mild cognitiveimpairment (MCI).

According to some embodiments, DELPHI's software architecture 300comprises five layers or modules, as outlined in FIG. 1:

-   -   i. Data acquisition module 301: configured for automated data        collection. A fixed stimulation protocol of TMS in varying        intensities and frequencies, introduced to specific        pre-determined locations on the skull, ensuring accuracy of the        acquired TEP data;    -   ii. Online data check module 302: configured for automated and        continuous evaluation of the collected data quality for optimal        collection at minimum acquisition time. The online data check        ensures a continuous online feedback of data quality;    -   iii. Data pre-processing module 303: configured for automated        rapid cleaning of data following acquisition;    -   iv. Data analysis and features extraction module 304: configured        to provide measured signal features, which are extracted and        calculated for determining the relevant electrophysiological        parameters of DELPHI physiological profiling; according to some        embodiments, the analyzed data, the extracted features and        optionally classification results are stored in a database 305        for further use; according to some embodiments, the database is        configured to store and provide the analyzed data, the extracted        features and the classification results data collected from the        analysis of plurality of subjects “database subjects”.    -   v. Classification of population subgroups module 306, configured        to provide at least one of: numerical output data, color coded        images and classification features of clinical conditions.

According to some embodiments, DELPHI's electrophysiological parametersconstitute the subject network physiological profiling, which isdisplayed as numeric raw values. In order to enable diseaseclassification, all clinical and DELPHI extracted features are uploadedinto an anonymized cloud-based database 305 as part of the evaluation,this enables constant growth of DELPHI database and refinement ofsubgroups. Supervised machine learning algorithms enable incorporationof neurophysiological profile and clinical health status. Thereliability of DELPHI as a state and disease classification toolincreases with the growth in the quantity of collectedneuro-physiological biomarkers data.

Reference is now made to FIG. 2, demonstrating a flow chart forextracting at least part of the input feature elements, according tosome embodiments of the invention. The flow chart is demonstrating thatdata acquisition is performed automatically by introducing a sequence ofstimuli in changing intensities and frequencies 401, followed by abilayer data cleaning step of TMS artifact removal 402 and datafiltering 403. In the following, average response features of chargetransfer (area under the curve of sensor signal), slopes and latenciesare extracted 404, providing the single pulse and plasticity profile ofnetwork functionality 405. These physiological parameters are unifiedinto one multidimensional neuro-physiological DELPHI profile of brainnetwork functionality 406. Cortical network values may be translatedinto pseudo-colored coded image describing brain network functionality407.

According to some embodiments, and as demonstrated in FIG. 3 a newcomputer implemented method 500 is provided, configured for classifyinga brain status of a subject, from a neural activity response of thesubject to an induced TMS stimulation. The method comprising:

-   -   constructing 510 at least one machine learning classifier (MLC)        configured to classify a subject's brain status;    -   training 520 the MLC using a training set, the set comprising        pairs of training output-classification vectors and their        corresponding training input vectors, all extracted from a        database of subjects with known brain status classifications,        wherein:        -   each training output-classification vector is determined 541            based on at least one of a database-subject's known: MRI            readings, physician/s classification, cognitive test/s            evaluation, and any combination thereof;        -   each training input vector comprises features extracted from            a database-subject's brain neural activity response to the            induced TMS stimulation, the response is measured by an EEG            device;    -   applying 530 the trained MLC on an input vector comprising        features extracted from a tested-subject's neural activity        response to the induced TMS stimulation, to obtain an output        classification vector for the tested-subject's brain status.

According to come embodiments, the terms “brain State” and/or “brainstatus” refer to at least one selected from: brain's health,connectivity between the brain's areas, the brain's ability to processinformation, brain's plasticity, brain's activity and any combinationthereof.

According to some embodiments, each output classification vector andaccordingly each training output classification vector comprise featuresselected from:

-   -   brain MRI-T1: Gray matter and White matter volume and/or surface        of cortical and subcortical areas; referred as vector “V1”;    -   MRI-DWI (diffused tensor imaging): White matter measures of        Fractional anisotropy (FA) and Mean diffusivity (MD); referred        as vector “V2”;    -   physical status selected from: healthy/not healthy, the brain's        evaluated age, neurological disorders, neurodegetative disease,        Alzheimer, chronic pain, Dementia, small vessels disease,        Psychiatric disorders, depression, physical injury,        pathophysiological abnormalities, structural damage of the grey        matter, structural damage of the white matter, functional        damage, internal bleeding, balance between excitation and        inhibition in the regional cortical network, intra-cranial        pressure, cerebro-vascular accident (CVA), Basal ganglia injury,        brain stem injury, corticospinal track injury, frontal lobe        injury, temporal lobe injury, other brain area injuries,        diabetes, hypertension and any combination thereof; referred as        vector “V3”;    -   and any combination thereof.

According to some embodiments, the method 500 further comprising a stepof determining each training output-classification vector, based on atleast one of a database-subject's known: MRI readings, physician/sclassification, cognitive test/s evaluation.

According to some embodiments, the MLC comprises at least one moduleselected from:

-   -   a multi layered MLC;    -   a classification module (for categorization), configured for        separation of the extracted features into discrete        classification groups performed by, at least one selected from:        -   support vector machine (SVM),        -   decision trees, and        -   K-nearest neighbors;    -   a registration module, configured for continuous data        prediction, selected from:        -   linear and/or non-linear regression,        -   artificial neural network (NN), as demonstrated in FIG. 4,            and        -   adaptive fuzzy logic learning;    -   any combination thereof.

According to some embodiments, and as demonstrated in FIG. 4 the MLC 550comprises a multi layered neural network (NN) comprising plurality ofnodes 551, configured as: an input layer 560 for the training input 561or applied input 562 extracted from subject's EEG features; an outputlayer for the target training output 571 or output 572 for the subject'shealth classification; and hidden layers 580.

According to some embodiments, each of the input vectors and accordinglyeach of the training input vectors further comprises at least onefeature selected from: age, gender, known medical status, drugtreatment, blood pressure, and any combination thereof.

According to some embodiments, the TMS simulation frequency is selectedfrom:

-   -   below 0.5 Hz; noted herein as “single pulse” for a neural        response, which does not depend upon stimulations history; and    -   above 0.5 Hz; noted herein as “a paired pulse”, where the neural        response to pulses are affected by the previously provided        pulses, which indicates short term plasticity.

According to some embodiments, the TMS simulation frequency is selectedbetween 0.1 Hz and 20 Hz.

According to some embodiments, the method 500 further comprising stepsof:

-   -   receiving, via an EEG device, a neural activity response of a        subject's brain to the induced TMS stimulation to one or more        predetermined brain regions of a subject;    -   according to some embodiment, eight location are being sensed,        four at each side (right/left), and    -   extracting response features 542,552 from the subject's neural        activity response, as elements for an input vectors or a        training input vector.

According to some embodiments, the step of extracting 542,552 is atleast based on positive and negative peaks at the neural activityresponse, measured, for example, in the range of 25-300 millisecondsafter the TMS stimulation is provided; and wherein the response featurescomprise at least one of the response's:

-   -   signal amplitudes;    -   amplitude latencies;    -   signal components analysis, such as principle component analysis        (PCA), and independent component analysis (ICA);    -   slopes between positive and negative peaks;    -   charge transfer (area under the curve of sensor signal);    -   lag of signal phase from healthy signal;    -   signal correlation to a healthy subject signal model;    -   ratio between segments in the signal of the same sensor, when        TMS induced frequency is above 0.5 Hz (“paired”);    -   coherence between the signal of different sensors;    -   brain connectivity; and    -   any combination thereof.

According to some embodiments, connectivity and plasticity aredetermined in different ways. For example, measuring short termplasticity by comparing one pulse response to another for example.According to some embodiments, connectivity is determined by examiningresponse latencies and coherence between electrodes.

According to some embodiments, the step of extracting comprisesdetermining the positive- and negative-peaks, at time steps selectedfrom a group consisting of: about 60 milli-seconds (mSec), about 100mSec, about 180 mSec, and any combination thereof. According to someembodiments, the determining of the positive- and the negative-peaks,can be selected at other predetermined time steps.

According to some embodiments, the extracted slopes are provided betweendetermined positive and negative signal peaks, which are adjacent,thereby extracting peaks' relation. According to some embodiments, theextracted slopes are provided between the determined peaks, which maynot be adjacent.

According to some embodiments, the step of extracting features furthercomprises comparing the slope of the 60 mSec peak with the 100 mSec peak(60-100 slope), versus the slope of 100 mSec peak with 180 mSec peak(100-180 slope).

According to some embodiments, the step of extracting features furthercomprises comparing the 60-100 slope with the 100-180 slop.

According to some embodiments, the TMS is induced in several sequentialstimulations, each at different intensity.

According to some embodiments, the stimulated brain region is selectedfrom a group consisting: frontal, parietal, temporal, occipital (rightand left hemispheres) and any combination thereof.

According to some embodiments, the terms “early slope” and/or “earlyphase deflection (EPD)” refer to a slope of early response component,for example a component measured at 60-100 mSec after the TMSstimulation. According to some embodiments, the terms “late slope”and/or “late phase deflection (LPD)” refer to a slope of late responsecomponent, for example a component measured at 100-180 mSec after theTMS stimulation. According to some embodiments, the terms “correlationto ideal signal” and/or “wave form adherence (WFA)” refer to correlationof a signal from a sensor to healthy ideal form. According to someembodiments, the term “short term inhibitory plasticity” refers to therelation between a neural response to a first TMS pulse and a neuralresponse to a second TMS pulse, during inhibitory TMS stimulationprotocol, meaning TMS stimulation frequency ranging between 1 Hz to 5Hz. According to some embodiments, the term “short term excitatoryplasticity” refers to the relation between a neural response to a firstTMS pulse and a neural response to a second TMS pulse, during excitatoryTMS stimulation protocol, meaning TMS stimulation frequency providedabove 10 Hz. According to some embodiments, the term “charge transfer”refers to the area under the curve of sensor signal. According to someembodiments, the term “signal lag” refers to the lag of signal phasefrom healthy ideal signal.

According to some embodiments, the input vectors and accordingly each ofthe training input vectors comprise at least one feature extracted fromthe neural response to the TMS stimulation, the feature is selectedfrom:

-   -   early slope/late slope*correlation to ideal signal; for a TMS        frequency below 0.5 Hz (single pulse); referred as        “DELPhi_feature1”;    -   coherence between individual sensors; for a TMS frequency below        0.5 Hz (single pulse); referred as “DELPhi_feature2”;    -   short term inhibitory plasticity/short term excitatory        plasticity; for a TMS frequency above 0.5 Hz (paired pulse);        referred as “DELPhi_feature3”;    -   charge transfer*signal lag (from ideal); for a TMS frequency        below 0.5 Hz (single pulse); referred as “DELPhi_feature4”; and    -   any combination thereof.

According to some embodiments, the MLC training method is according toat least one method selected from:

-   -   each database-subject's DELPhi_feature1 and DELPhi_feature2 are        provided as training inputs, and their respective vectors V1 and        V2 are provided as training outputs (demonstrated in FIG. 6A);    -   each database-subject's DELPhi_feature4 is as training input,        and their respective output vector V3 is provided as training        output (demonstrated in FIG. 6B);    -   each database-subject's DELPhi_feature3 together with subject's        known medication/drug treatment are provided as training inputs,        and their respective vector V3 provided as training output        (demonstrated in FIG. 6C); and    -   any combination thereof.

Reference is now made to FIGS. 5A and 5B schematically, whichdemonstrate the two MLC use phases: Training (FIG. 5A) and Application(FIG. 5B), according to some embodiments of the invention. At theTraining phase 600, the supervised MLC 710 is provided with:

-   -   features extracted from the EEG signals 610 (measured after        providing TMS stimulation/s) of database subjects, provided as        training inputs; and    -   the database-subjects' known imaging information and clinical        data, provided as training outputs.

At the Application phase 800, the trained supervised MLC 720 is providedwith EEG signals 810 of a subject (a new subject with no identifiedclassifications), measured after providing TMS stimulation/s, providedas an input to obtain an output of evaluated imaging information and/orclinical data.

Reference is now made to FIGS. 6A, 6B and 6C schematically demonstratingexamples for different Training phases, according to some embodiments ofthe invention.

According to some embodiments, and as demonstrated in FIG. 6A, the MLCcomprises a registration based supervised MLC 711 provided with:

-   -   features 611 extracted from the EEG signals 610 (measured after        providing TMS stimulation/s) of database subjects, provided as        training inputs; and    -   the brain MRI-T1: Gray matter and White matter volume or surface        of cortical and subcortical areas, referred as vector “V1” 622,        and the brain MRI-DWI (diffused tensor imaging): White matter        measures of Fractional anisotropy (FA) and Mean diffusivity        (MD), referred as vector “V2” 623; all extracted from the        database subjects' known MR imaging information 621, and        provided as the MLC training outputs.

According to some embodiments, and as demonstrated in FIG. 6B, the MLCcomprises a supervised MLC 712 provided with:

-   -   features 612 extracted from the EEG signals 610 (measured after        providing TMS stimulation/s) of database subjects, provided as        training inputs; and    -   database subjects known medical history and classifications 622,        provided as the MLC training outputs.

According to some embodiments, and as demonstrated in FIG. 6C, the MLCcomprises a supervised MLC 713 provided with:

-   -   features 613 extracted from the EEG signals 610 (measured after        providing TMS stimulation/s) of database subjects together the        database subjects' known medication/drug treatment 614, provided        as training inputs; and    -   database subjects known medical history and classifications 623,        provided as the MLC training outputs.

Reference is now made to FIGS. 7A and 7B schematically demonstrating anexample for the Application phase of integrated MLC modules (FIG. 7A)and their method steps (FIG. 7B), according to some embodiments of theinvention.

According to some embodiments, and as demonstrated in FIG. 7A, thetrained MLC trained MLC modules 721,722,723, following the trainingphase respectively demonstrated in FIGS. 6A-6C, are applied withselected input vectors 811,812,813 extracted from a subject's (a newsubject with no identified classifications) the EEG signals 810 andoptionally drug treatment 814; the outputs of the trained modules721,722,723 is integrated in the integrated output classification module830, which is configured to provide clinical and imaging classification820.

FIG. 7B demonstrates the method steps for the integrated Applicationphase, using the MLC modules demonstrated in FIG. 7A, according to someembodiments of the invention. The method steps comprising:

-   -   recording/receiving 1810 EEG signals 810 and optionally        receiving 1814 the drug treatment 814, of a subject (a new        subject with no identified classifications);    -   extracting 1811,1812,1813,1814 input vectors from the        received/recorded data 810,814, for each of the trained MLC        modules 721,722,723;    -   selectively applying 1721,1722,1723 the trained MLC modules        721,722,723 on their respective input vectors 811,812,813,814        for obtaining 1821,1822,1823 their respective output classes        821,822,823;    -   applying 1830 an integrated classifier 830 on the obtained        output classes 821,822,823, for obtaining 1820 an integrated        brain status output 820.

According to some embodiments an apparatus 100 is provided configured toevaluate brain state of a subject, the apparatus comprising:

-   -   a directed inspective/diagnostic stimulation unit 202,        configured to induce TMS diagnostic stimulation to a        predetermined brain region of the subject;    -   a brain activity EEG sensor unit 203, configured to measure a        neural activity response to the diagnostic stimulation induced        by the directed brain stimulation unit; and    -   at least one processing circuitry 201 and at least one memory        unit, in wired or wireless communication with the brain activity        sensor unit, the processing circuitry is configured to execute        the method steps according to any one of the preceding method        embodiments for classifying a brain status of a subject, from a        neural activity response of the subject to an induced TMS        stimulation.

According to some embodiments a transient and/or non-transient computerreadable medium (CRM) is provided, such that when loaded into a memoryof a computing device and executed by at least one processor of thecomputing device, is configured to execute at least the steps of thecomputer implemented method according to any one of the preceding methodembodiments for classifying a brain status of a subject, from a neuralactivity response of the subject to an induced TMS stimulation.

In the following non-limiting examples are provided, to demonstrate atleast some of the provided embodiments.

Examples

Experiment Methods

TRANSCRANIAL MAGNETIC STIMULATION: TMS was performed with a MagPro R30stimulator (MagVenture, Denmark) and an MCF-B65-HO figure-8 Coil(MagVenture, Denmark). Resting motor threshold (rMT) was obtained at thebeginning of every session by stimulating the left motor cortex anddefined as the intensity that produced a visible twitch in adductorpolicis brevis on 50% of stimulations (Rosanova et al.). Each test-runentailed about 420 pulses (biphasic pulses at 280 mSec pulse width) atranging intensities, from 50% to 120% of the rMT Stimulation varied infrequency of continues stimulation of 0.1 Hz, 1 Hz and paired pulsestimulation on 20 Hz with inter-burst-interval of 30 seconds. A thin(0.5 mm) foam pad was attached to the TMS coil to minimize electrodemovement and bone-conducted auditory artefact. Participants wereinstructed to keep their eyes closed throughout the examination. TMScoil was placed over the left side of the cortical motor (M1) region inthe appropriate manner (The coil was held tangentially to the scalp at45° to the midline) and rMT was measured to ensure proper placement ofthe stimulating coil. Intensities of stimulation increase gradually from50% of the personalized determined resting motor threshold (rMT) up to120% of the rMT in a specific and predetermined protocol of stimulation.Evoked response of the stimulated hotspot (weighted average of threesensors in the proximity of the point of stimulation: C3, C5, CP1) andcontralateral to the stimulated hotspot (right M1: C4, C6, CP2) wereanalyzed and an I/O curve of regional response was described perindividual. Clarification: the EEG response to TMS stimulation iscalculated as the average signal recorded from three electrodes in theleft motor area hemisphere. The electrodes are named: C3, C5 and CP1.The hotspot electrodes in the right motor area hemisphere are named: C4,C6 and CP2.

FIGS. 8B, 8C, 8D and 8E demonstrate examples for intensity dependent TMSevoked response in young healthy subjects; FIG. 8B demonstratesrepresentative grand average traces of left M1 hotspot (upper bundle ofsubjects) and right M1 hotspot (lower bundle of same subjects); FIG. 8Cdemonstrates mean healthy population charge transfer change by stimuliintensity of left hotspot (green) and right hotspot (turquoise); FIG. 8Ddemonstrates mean healthy population P60-N100 Slope change by stimuliintensity of left hotspot (green) and right hotspot (turquoise); andFIG. 8E demonstrates mean healthy population N100-P180 slope change bystimuli intensity of left hotspot (green) and right hotspot (turquoise);in the examples 100%=2 Tesla.

SHAM STIMULATION: for sham (Placebo) TMS stimulation the 8-figure coilwas placed over the left side of the cortical motor (M1) region, in theexact same orientation as for non-sham stimulation; the purpose is todemonstrate the lack of neuro-physiological response in the absence realTMS stimulation. After placement, the coil was moved 1 cm away from thescalp and a silicone cube (1 cm×3 cm) filled with artificial cerebralspinal fluid (ACSF) was placed in between. Stimulation protocol(duration, intensities and frequencies) was maintained the same as innon-sham. The purpose of the sham stimulation was to demonstrate thelack of neuro-physiological response in the absence real TMSstimulation.

ELECTROENCEPHALOGRAPHY: 32-channel EEG data was obtained using two32-channel TMS compatible BrainAmp DC amplifiers (5 kHz sampling rate;±16.384 milli-volt measurement range; analog low pass filter 1 kHz).These were attached to the Easy EEG cap (EasyCap GmbH, Germany) withAg—AgCl electrodes. Electrode impedances were kept below 5 kilo-Ohm. Thereference and ground electrodes were affixed to the ear lobes. EEG datawas recorded using BrainVision Recorder software (Brain Products GmbH,Germany). All data was pre-processed and analyzed using the fullyautomated developed DELPhi algorithm and carried out on MATLAB (R2016b,The Mathworks Inc, MA).

Study Subjects Group 1:

Four study groups were evaluated, each group was defined as followed:

(a) young and healthy 25-40 years old (yo),

(b) healthy adults, 50-70 years old;

(c) healthy adults over 70 years old;

(d) early dementia diagnosed subjects, over 65 years old.

Healthy subjects in all ages were defined as follows: determined healthyby a physician, with a clear MRI scan, not taking any prescribedmedication, normal computerized cognitive evaluation over 95 mean globalscore normalized to age related population, and clear neurocognitiveevaluation by neuropsychologist.

According to some embodiments, mild Dementia subjects were defined asfollows: determined having dementia diagnosis by a physician andneuropsychologist, Montreal cognitive assessment (MoCA) score between 11and 21, Neurtrax BrainCare computerized testing memory test at least 1.5standard deviations (STDV) below age related norm, and at least oneother test of executive function, visual spatial memory or verbalfunction, with minimum of 1.5 STDV below norm.

All patients had MRI scans, 1-2 weeks before DELPhi evaluation. Imagingwas done with a 3 Tesla system (20 channels, MAGNETOM Skyra, SiemensMedical Solutions). The MRI protocol included T2 weighted, T1 weighted,FLAIR and susceptibility weighted imaging (SWI) sequences. All scanswere analyzed by a neuro-radiologist.

Assessment of cognitive functions was performed by trainedneuropsychologists using the standard Montreal cognitive assessment(MoCA) test and NeuroTrax computerized BrainCare cognitive battery tests(NeuroTrax Corp., TX) (Dwolatzky et al., 2003).

DATA ANALYSIS: The analysis was performed online automatically using thecurrently provided DELPhi acquisition and analysis methods. According tosome embodiments, charge transfer was measured as the area under thecurve between 30 and 300 mSec of the evoked response. According to someembodiments, Inhibition-excitation balance (relation) was determined bycalculating the slopes of the evoked response. Slopes were measuredbetween 60 mSec and 100 mSec (P60-N100) and between 100 mSec and 180mSec (N100-P180) (as demonstrated in FIGS. 8D-8E); e.g. slop of astraight line connecting two points. According to some embodiments,short term plasticity, which corresponds to the network ability tochange the ratio between excitation and inhibition, was expressed by theratio between slopes evoked in different frequencies of stimulation(e.g. 0.1, 1, 20 Hz) (as demonstrated in FIGS. 9A-9D). Values areexpressed as mean+/−standard error (SE).

FIGS. 9A, 9B, 9C and 9D demonstrate frequency dependent TMS evokedresponse in young healthy subjects. FIG. 9A demonstrates representativegrand average traces of M1 left hotspot (upper) and right M1 hotspot(lower); green color for 0.1 Hz, orange color for 1 Hz, blue color for20 Hz; FIG. 9B demonstrates representative charge transfer change byfrequency of stimulation of M1 left hotspot (green) and M1 right hotspot(turquoise); FIG. 9C demonstrates representative N100-P180 Slope changeby frequency of stimulation of M1 left hotspot (green) and M1 righthotspot (turquoise); and FIG. 9D demonstrates representative P60-N100slope change by frequency of stimulation of M1 left hotspot (green) andM1 right hotspot (turquoise).

STATISTICAL ANALYSIS: Statistical data analysis was performed usingGraphPad Prism 7. Test retest measures were compared by Pearson'scorrelation and Bland-Altman plot. Error bars shown in the figuresrepresent standard error of the mean (s.e.m.). The number of subjects isdefined by N. One-way ANOVA analysis with post hoc Tukey was used tocompare subject groups. Student's un-paired t-test has been used wereused to compare two. *p<0.05; **p<0.01; ***p<0.001, ns—non-significant.

Experimental Results—Group 1

According to some embodiments, the above mentioned methods are providedto identify and characterize the physiological profile of brain networkfunctionally in order to differentiate healthy populations, young, adultand aging, from brain network functionally characterizing pathologicallyabnormal brain functionality.

Neuronal network function is characterized by a tightly maintainedbalance of excitation and inhibition correlated with levels of activity.The balance between excitation and inhibition reflects the strength ofthe network. Response to TMS depends on the excitation-inhibitionbalance of the cortical network (Silvanto et al. 2008; Rogasch JNeurophysiol 2012). According to some embodiments, in order to assessthe strength of the network an Input-Output (I/O) function curve isdefined per individual subject, referring to the relationship betweenthe excitatory input to the cortical region and the strength of thegenerated TMS evoked EEG response. A brain network I/O curve, mainlyrepresents a sigmoidal function, which characterized according to soembodiments, by connecting its edge points (maximum and minimum) therebydefined by two components: the threshold and the gain (slope).

Reference is made again to FIGS. 8B-8E, which demonstrate intensitydependent TMS evoked response in young healthy subjects. Evaluatingbrain network strength in healthy population (25-45 years old, definedas healthy by a physician, with no abnormalities in an MRI, not takingany prescribed medication, normal cognitive evaluation) reveals anincrease in the evoked response correlated with the increase in theintensity of stimulation (FIG. 8B, different colors for differentintensities). A single TMS pulse delivered over the left primary motorcortex (M1) results in a sequence of positive and negative EEG peaks aspreviously reported (Ilmoniemi and Kicic 2010) at specific latencies inboth hemispheres (left—ipsilateral to stimuli; right—contralateral tostimuli). Most pronounced features of response are the positive peaks at60 mSec and 180 mSec and a negative peak at 100 mSec (P60, N100, P180)following stimuli, as demonstrated in FIG. 8B. Severalelectrophysiological parameters can be quantified from the evokedresponse. The first, well established electrophysiological parameter, isthe charge transfer of evoked response (Q) calculated as the area underthe curve.

As expected, the charge transfer, demonstrated in FIG. 8C, increaseswith the increased intensity of stimulation in both hemispheres. Theleft, stimulated hemisphere, shows a stronger intensity dependentincrease in charge transfer with an average total increase of 180% inthe 55% intensity compared to the 30% intensity (of maximal deviceintensity of stimulation), p<0.0001 in left hotspot linear regressionslope 11,445 (R²=0.72), and an average total increase of 130%, p<0.05 inright hotspot linear regression slope 4908 (R²=0.71), p<0.05 N=33.

Intensity dependent effects on the three main peaks (P60, N100, P180)represent patterns of the individual physiological response giving animportant understanding on changes in cortical excitability andinhibition, however, these peaks (P60, N100, P180) vary in theirspecific timing (50-70 mSec, 90-120 mSec, 150-210 mSec respectively) forexample, due to any cortical changes as age dependent atrophy.Therefore, the slope of responses (P60-N100; N100-P180) were measuredgiving a more robust and reliable parameters to monitor, understandingany functional change in the network will be reflected as change in themeasured slopes. P60-N100 slope, as demonstrated in FIG. 8D, is enhancedwith the increased intensity of stimulation in both hemispheres, with anaverage total decrease of 376% in 55% compared to 30% p<0.001 in lefthotspot linear regression slope −0.015 (R²=0.92), and an average totaldecrease of 172%, P<0.001 in right hotspot linear regression slope−0.005 (R²=0.78), p<0.01 N=33. N100-P180 slope as demonstrated in FIG.8E, does not show any significant intensity dependent change above 30%intensity of stimulation with an average total decrease of 13% in 55%compared to 30% p>0.05 in left hotspot linear regression slope −0.0005(R²=0.31), and 15% increase, P>0.05 in right hotspot linear regressionslope 0.0007 (R²=0.5), p<0.05 N=33.

As mentioned above, a sham-placebo stimulation was performed asdescribed in the methods section. Sham stimulation revealed that allreferenced parameters are relevant brain network neuronal responses, asdemonstrated in FIG. 10A.

FIGS. 10A, 10B, 10C, 10D and 10E demonstrate Sham Controlled TMS evokedstimulation in healthy subjects, displayed via frequency dependentbehavior, indicating reliability of the measured parameters andtherefore mechanisms of network short term plasticity (N=5; p<0.01).FIG. 10A demonstrates representative single pulse (0.1 Hz) withrepresentative average traces of left M1 hotspot in healthy subjects;black color for real stimulation and green color for sham stimulation;FIG. 10B demonstrates representative inhibitory stimulation (1 Hz)representative grand average traces of left M1 hotspot; black color forreal stimulation and green color for sham stimulation; FIG. 10Cdemonstrates representative excitatory (20 Hz) representative grandaverage traces of left hotspot healthy subjects; black color for realstimulation and green color for sham stimulation; FIG. 10D demonstratesrepresentative grand average traces of real TMS stimulation at 0.1 Hz, 1Hz, 20 Hz (black, green, blue respectively) over the left M1 hotspot;and FIG. 10E demonstrates representative grand average traces of shamTMS stimulation at 0.1 Hz, 1 Hz, 20 Hz (black, green, blue respectively)over the left M1 hotspot).

Short term plasticity is evaluated in electrophysiology by changing thefrequency of stimulation and measuring the ‘history dependency’ of theevoked response. TMS stimulation were introduced in two intensities, suband suprathreshold (80% and 120% of rMT respectively). Three stimulationfrequencies were tested, 0.1 Hz (single pulse, showing no ‘historydependency’ (R. Chen May 1997; Neurology), 1 Hz (low frequency(LFrTMS)—evoking inhibition of response in a mechanisms that may bysimilar to long term depression ˜LTD (Muellbacher W, ClinicalNeurophisiology 2000; R. Chen May 1997; Neurology; Fitzgerald et al,clinical neurophysiology 2006) and 20 Hz (high frequency(HFrTMS)—evoking excitation of evoked response (Fumiko Maeda et al,clinical neurophysiology, May 2000; MauroGarcia-Toro et al. PsychiatryResearch: Neuroimaging 2006; Chul Kim et al. Ann Rehabil Med. 2014;Mansur C G, Neurology 2005; Fregni F, Stroke 2006; Peinemann A, ClinNeurophysiol 2004)).

Reference is made again to FIGS. 9A-9D, which demonstrate frequencydependent TMS evoked response in young healthy subjects. FIG. 9Ademonstrates that both hemispheres (ipsi- and contra-lateral to stimuli)demonstrated a similar frequency dependent behavior of evoked responsein both intensities of stimulation (sub- and supra-threshold, data notshown). A significant decrease in all measured parameters was recordedin 1 Hz stimulation compared to 0.1 Hz. Charge transfer, as demonstratedin FIG. 9B was reduced in 1 Hz by 39% in left hotspot and 54% in righthotspot in subthreshold intensity (p<0.001) and 30% in left hotspot and38% in right hotspot in supra-threshold intensity (p<0.001; N=33);N100-P180 slope, as demonstrated in FIG. 9C, was reduced by 83% in 1 Hzstimulation in left hotspot and 73% in right hotspot in subthresholdintensity (p<0.001) and 78% in left hotspot and 51% in right hotspot insupra-threshold intensity (p<0.01; N=20). P60-N100 slope, asdemonstrated in FIG. 9D, was reduced in 1 Hz by 40% in left hotspot and80% in right hotspot in subthreshold intensity (p<0.001) and 32% in lefthotspot and 42% in right hotspot in supra-threshold intensity (p<0.001;N=20. The reduction in response evoked in 1 Hz compared to 0.1 Hzreflect network inhibition. 20 Hz stimulation shows no significantchange in charge transfer compared to 0.1 Hz and a significant increasein response of 150% in left hotspot and 255% in right hotspot comparedto 1 Hz in subthreshold intensity (P<0.001, N=20) (FIG. 6B). N100-P180slope was not significantly changed compared to 0.1 Hz or 1 Hz; P60-N100slope significantly changed from negative to positive evoked by 20 Hzstimulation both hemispheres hotspots in subthreshold and suprathresholdintensities compared to 1 Hz and 0.1 Hz (p<0.001; N=20) (FIGS. 9C, 9D)reflecting the mechanism of excitatory response evoked by high frequencyof stimulation. The most prominent frequency dependent change in evokedresponse was demonstrated between 0.1 Hz and 1 Hz of stimulation.

In order to establish system credibility, measured data reproducibilitytest was performed. Test-retest, evaluation of the collected andanalyzed parameters (charge transfer, slopes of evokes response andshort-term plasticity (STP) calculated as the ratio between measuredparameters in 1 Hz and 0.1 Hz) was performed. Results demonstrate highreliability and reproducibility of the DELPhi analyzed physiologicalparameters displaying Pearson's correlation r significantly higher than0.9 in all measured parameters (FIG. 11A-E) and a Bland-Altamn plot with95% limits of agreement for each parameter.

FIGS. 11A-11E demonstrate representative test (SN1) vs. re-test (SN2) ofTMS evoked response. 12 healthy subjects (N=12) were evaluated in twosessions (session 1—SN1; session 2—SN2), performed at least 24 hoursapart and up to one week; black dots represent the ratio between the twosessions of the same subject. FIG. 11A demonstrates mean wave formadherence (WFA)-correlation to healthy of a subject's SN1 versus SN2r=0.92, p<0.0001. FIG. 11B demonstrates mean early phasedeflection/early slope (ePD) of a subject's SN1 versus SN2 r=0.93,p<0.0001. FIG. 11C demonstrates mean late phase deflection/late slope(LPD) of a subject's SN1 versus SN2 r=0.95, p<0.0001. FIG. 11Ddemonstrates mean short term plasticity (STP) of a subject's SN1 versusSN2 r=0.9, p<0.0001. FIG. 11E demonstrates mean connectivity coherencemap (CCM) of a subject's SN1 versus SN2 r=0.85, p<0.001. Accordingly,FIGS. 8A-8E demonstrate the high reproducibility and reliability of themeasurement performed by DELPhi.

FIGS. 12A-12E demonstrate representative test (SN1) vs. re-test (SN2) ofTMS evoked response according to Bland-Altman plot. The black dotsrepresent 12 different subjects. The difference between values measuredat the two sessions are plotted against their mean (the middledotted-line), for each subject (black dots). The dotted lines representthe mean (middle dotted line) and limits (upper/lower) of agreement(LoA) of the differences between the two sessions across subjects. FIG.12A demonstrates mean wave form adherence (WFA)-correlation to healthyof a subject's SN1 versus the subject's SSN2, per each subject; FIG. 12Bdemonstrates mean early phase deflection/early slope (ePD) of a subjectSN1 versus the subject's SN2, per each subject; FIG. 12C demonstrateslate phase deflection/late slope (LPD) of a subject's SN1 versus thesubject's SN2, per each subject; FIG. 12D. demonstrates mean short termplasticity (STP) of a subject's SN1 versus the subject's SN2, per eachsubject; FIG. 12E demonstrates mean connectivity coherence map (CCM) ofa subject's SN1 versus the subject's SN2, per each subject.

Based on the provided ability to measure physiological properties ofcortical brain network functionality, in a reliable manner, according tosome embodiments a tool is provided configured to examine theseparameters' change with age, and differentiate this normal-healthyphysiological process from an abnormal, pathological, degenerativedeterioration on brain network functionality. Accordingly, the presentinvention provides a new clinical tool for evaluating and monitoringbrain aging and health. The same paradigm of data acquisition andanalysis was performed on four populations:

(a) healthy young, 25-40 years old;

(b) healthy elderly, 50-70 years old;

(c) healthy elderly, over 70 years old; and

(d) early dementia diagnosed, over 65 years old.

According to some embodiments, the ratio between the measured slopes ofthe evoked response (P60-N100; N100-P180) reflect the balance betweenexcitation and inhibition in the regional cortical network. Corticalexcitation-inhibition balance changes during normal aging andneurodegenerative pathological deterioration (Legon W et al, CerebralCortex, 2016; Grady C L et al, 2003, Hippocampus; Wang L, 2010,Neuroimage; Mormino E C, 2011, Cereb Cortex). Hence, examination of theregional slopes ratio is provided as a significant physiologicalmeasurement tool for monitoring brain health through aging.

Reference is made to FIGS. 13A-13D, which demonstrate a significant agedependent change in the excitation-inhibition balance (relation), whichwas measured in all the measured cortical regions, and is reflected as achange in the ratio between slopes of the evoked response. FIGS. 13A-13Ddemonstrate representative N100-P180 slope vs. P60-N100 slope. FIG. 13Ademonstrates representative temporal hotspot; FIG. 13B demonstratesrepresentative parietal hotspot; FIG. 13C demonstrates representativefrontal hotspot; and FIG. 13D demonstrates representative occipitalhotspot; full circle—left hotspot; empty circle—right hotspot; bluecolor for young healthy subjects (25-45 years old; N=40); green colorfor adult healthy subjects (50-70 years old; N=34); yellow color for oldhealthy subjects (over 70, N=18); and red color early Dementia subjects(over 70; N=20).

Subject population clustering demonstrates that the P60-N100 slopeparameter is a significant indicator for Dementia in both right- andleft-frontal and parietal areas. Dementia subjects display a significantchange of the slope from positive to negative values (p<0.001) versusthe healthy population (all ages), with a 420% change in frontal areas(FIG. 13C) and 480% change in parietal areas (FIG. 13B), relative to itsage related control group of healthy subjects over 70. The change of P60for healthy population to N60 for the demented population is illustratedin FIGS. 14A-14C. The N100-P180 slope displays a significant change indemented population compared to all healthy groups, with a decrease of60% to 95% in all areas, most pronounced in frontal (FIG. 13C) andparietal (FIG. 13B) areas (p<0.0001). Another significant difference isdemonstrated between young and old (over 70) population, in the frontaland temporal areas (p<0.01). Taken together the slopes P60-N100 andN100-P180 provide a robust clustering tool for defining brain health andage dependent changes, where slopes ratio of evoked response is used forevaluating age dependent brain functional changes.

Reference is made to FIGS. 15A-15D, which demonstrate the Ratio betweenSTP of charge transfer (STP-Q) and STP of the N100-P180 slope, measuredover the four cortical regions (Temporal, parietal, frontal andoccipital). FIGS. 15A-15D demonstrate representative short termplasticity of charge transfer as a function of short term plasticity ofN100-P180 Slope; FIG. 15A demonstrates representative temporal hotspot;FIG. 15B demonstrates representative parietal hotspot; FIG. 15Cdemonstrates representative frontal hotspot; and FIG. 15D demonstratesrepresentative occipital hotspot; full circle—left hotspot; emptycircle—right hotspot; blue color for young healthy subjects (25-45 yearsold; N=40); green color for adult healthy subjects (50-70 years old;N=34); yellow-color for old healthy subjects (over 70, N=18); and redcolor for early Dementia subjects (over 70; N=20).

According to FIG. 15A-15D the STP-Q reveals significant reduction inleft and right temporal areas (FIG. 15A) of demented population comparedto healthy groups of all ages (p<0.05), while the other areas (FIGS.15B-15D) reveal significant reduction in demented subject only comparedto young and elderly population (50-70) (p<0.01), suggesting that STP-Qchanges are age dependent in all brain areas, but increase in temporalareas during dementia. As demonstrated, the N100-P180 STP is reduced indemented population compared to all healthy groups, most significantlyin frontal and temporal areas (FIGS. 15A and 15C) with a significantchange from age related healthy controls (p<0.001), age dependentchanges are less pronounced and non-significant. Therefore, according tosome embodiments, STP measures are provided as indicators of frontal andtemporal plasticity changes, related specifically to dementia.

According to some embodiments, the invention provides to a system andmethods for evaluation of brain abnormalities, such as structural damageof grey or white matter. The system comprises EEG and Magneticstimulation for the evaluation of evoked brain response provoked by TMSpulse. By measuring EEG response to single pulse TMS stimulation ontocertain brain areas (one or more), a brain network signature response isacquired that can be compared to healthy normal brain response, forevaluation of brain structural damage of grey or white matter.

According to some embodiments, the TMS stimulation may be delivered todifferent brain circuits, at frequency of 0.1 to 100 Hz in varyingintensities from 0.05 to 5 Tesla.

According to some embodiments, the EEG response evoked by TMS ismeasured at a time step selected between 0-400 milliseconds from TMSstimulation end.

According to some embodiments, the EEG response contains negative andpositive peaks that are consistent between individuals with healthybrain structure.

According to some embodiments, the EEG response peaks to the TMSstimulation contain the peaks of P30, N45, P60, N100 and P180 and/orother consistent peaks.

According to some embodiments, the EEG response from normally structuredbrain has consistent signature in a peak timing amplitude and slopes, inthe range of +/−standard deviation from average normal healthypopulation in each recording EEG electrode.

According to some embodiments, the EEG response from abnormallystructured brain, caused either by abnormalities in the white matter orthe grey matter of different brain area or intra-cranial pressureinduced activity changes, is inconsistent with normal healthy brainsignature and can vary in peak timing, amplitude or slope.

According to some embodiments, the EEG response from abnormalstructured, in the grey or white matter or changes in intracranialpressure in different electrodes can display inconsistent response andcan vary in peak timing, amplitude or slope.

Reference is made to FIGS. 16A-16B, which demonstrate the correlationbetween the mean N100-P180 slope and the mean P60-N100 slope. FIG. 16Ademonstrates the stimulation of the left motor cortex and FIG. 16Bdemonstrates the stimulation of the right motor cortex, both in healthy25-45 years old, healthy 50-70 years old, healthy over 70 years old, andCVA cases with a brain injury in either the left corticospinal track,left frontal lobe and left temporal lobe.

Reference is made to FIGS. 17A-17B, which demonstrate the correlationbetween the mean N100-P180 slope and the mean P60-N100 slope. FIG. 17Ademonstrates the stimulation of the left motor cortex and FIG. 17Bdemonstrates the stimulation of the right motor cortex, both in healthy25-45 years old, healthy 50-70 years old, healthy over 70 years old, andCVA cases with a brain injury in either the basal ganglia or the brainstem.

Reference is made to FIGS. 18A-18B, demonstrate the correlation betweenthe mean N100-P180 slope and the mean P60-N100 slope. FIG. 18Ademonstrates the stimulation of the left motor cortex and FIG. 18Bdemonstrates the stimulation of the right motor cortex, both in healthy25-45 years old, healthy 50-70 years old, healthy over 70 years old, andCVA cases with a brain injury in either the left corticospinal track,left frontal lobe and left temporal lobe.

Reference is made to FIGS. 19A-19B, which demonstrate the correlationbetween the area under the plasticity curve and N100-P180 slope ofplasticity. FIG. 19A demonstrates the stimulation of the left motorcortex and FIG. 19B demonstrates the stimulation of the right motorcortex; both in healthy 25-45 years old, healthy 50-70 years old,healthy over 70 years old, and CVA cases with a brain injury in eitherthe left corticospinal track, left frontal lobe and left temporal lobe.

FIGS. 20A and 20B demonstrate short term plasticity differences betweenhealthy intact brain and specific brain injuries in midbrain and brainstem, according to some embodiments of the invention;

FIGS. 21A and 21B demonstrate short term plasticity differences betweenhealthy intact brain and specific brain injuries in right side,according to some embodiments of the invention.

STUDY SUBJECTS GROUP 2: Four groups were included in the followingdemonstrations. Groups are defined as follows:

-   -   (a) young and health, 25-45 years old, (N=30; mean age: 35,        standard deviation (STDV): 6.6);    -   (b) healthy adults, 50-70 years old, (N=30 mean age: 61; STDV:        5.9);    -   (c) healthy elderly, >70 years old; (N=17; mean age: 75.4; STDV:        6.6);    -   (d) patients diagnosed with MCI/mild dementia, >65 years old,        (N=20; mean age: 75.2, STDV: 4.3).

According to some embodiments, healthy subjects were determined per: aphysician assessment, absence of significant abnormal findings in MRIscan, no central nerve system (CNS) directed prescribed medicationtreatment, and a neurocognitive evaluation by neuropsychologist, whichincludes a normal computerized cognitive evaluation (over 95 mean globalscore normalized to age related population).

According to some embodiments, MCI/mild dementia was defined as follows:neuropsychologist evaluation, Montreal Cognitive Assessment (MoCA) scorebetween 11 and 22, Neurotrax BrainCare computerized testing memory indexscore at least 1.5 standard deviations (STDV) below age related norm andat least one more cognitive domain (out of 5attention/memory/Information processing speed/motor function/Executivefunction) index score with minimum of 1.5 STDV below norm. All patientsunderwent a brain MRI scan 1-2 weeks before DELPHI evaluation. Imagingwas performed with a 3 Tesla system (20 channels, MAGNETOM Skyra,Siemens Medical Solutions). The MRI protocol included T2 weighted, T1weighted, FLAIR and susceptibility weighted imaging (SWI) sequences. Allscans were analyzed at a central lab by a neuro-radiologist. Assessmentof cognitive functions was performed by trained neuropsychologists usingthe MoCA test and NeuroTrax computerized BrainCare cognitive batterytests (NeuroTrax Corp., TX).

Experimental Results—Group 2

AGE DEPENDENT CHANGES IN BRAIN NETWORK FUNCTIONALITY: The purpose is toexamine whether the electrophysiological parameters described abovechange during aging in humans and differentiate normal-healthyphysiological process from pathological, degenerative deterioration ofbrain network functionality. It is known that corticalexcitation-inhibition balance changes with normal aging and duringneurodegenerative pathological deterioration. According to someembodiments, by examining the regional excitatory-inhibitory componentsof evoked response can provide a significant physiological measure formonitoring brain health through aging.

According to some embodiments, physiological patterns of networkfunctionality that best distinguished between groups (aging and MCI/milddementia) cab be automatically identified by the DELPHI algorithmanalysis and are presented in the following.

According to some embodiments, evaluation of age dependent changes innetwork strength is provided. The DELPHI analysis of single pulseresponse is displayed as connectivity matrixes of averaged age groups,as demonstrated in FIGS. 22A-22D. The data in FIGS. 22A-22D presentsaveraged population response of:

(a) 25-45 years old (yo) healthy group;

(b) 50-70 years old healthy group;

(c) over 70 years old healthy group;

(d) over 70 years old diagnosed with Mild dementia.

Correlation values are presented as consecutive color-coded bar, bluecolor demonstrates high correlation, while red color demonstrates lowcorrelation. According to some embodiments, a decrease in signalcoherence is observed with age. FIG. 22A demonstrate young 25-45 yo.matrix it displays high correlation values, which are slightly decreasedin the 50-70 yo. group as demonstrated in FIG. 22B, and furtherdecreases in the 70-85 yo. group as demonstrated in FIG. 22C. Asignificant difference is observed in the MCI/mild dementia patientsdemonstrated in FIG. 22D, which display a sparse connectivity map withpronounced decrease in interhemispheric and regional coherence betweenfrontal and temporal and parietal areas.

According to some embodiments, and as demonstrated in FIGS. 23A-23F,DELPHI is configured to identify two features that display a significantage dependent decrease in both early (as in FIG. 23A for P60-N100) andlate (as FIG. 23B for N100-P180) components of evoked response (datadisplayed for right hemisphere, contralateral to stimulation, parietalcortex).

According to some embodiments, the comparison of the two age comparablegroups of healthy elderly and patients diagnosed with MCI/mild dementia,demonstrates a significant difference in both components, particularlyin the late slope of evoked response (N100-P180), as demonstrated inFIG. 23B.

According to some embodiments, the group averaged regional ratio,between these two slopes (early and late) of evoked response, displays asignificant, age dependent, change with pronounced differentiationbetween normal healthy aging, and MCI/mild dementia, over the frontal,parietal, temporal and occipital cortical areas, as respectivelydemonstrated in FIGS. 23C-23F demonstrate color-coded images of subjectsfrom the representative four study groups. FIG. 23C demonstrates a38-year-old healthy subject with high and uniform ratio between the lateand early slope of evoked response reflected as a homogeneous bluecolored brain. FIG. 23D demonstrates a decline in the measured ratio isdemonstrated with age, translated into light blue colored brain of a58-year-old subject, representing the healthy 50-70 age group, and agreen-yellow colored brain for a 75-year-old representing the healthy70-85 years old group, as demonstrated in FIG. 23E. The MCI/milddementia group, represented by a 71 years-old subject, displays anegative high ratio between late and early slopes of evoked response,reflected as orange colored cortical brain network functionality, asdemonstrated in FIG. 23F.

AGE DEPENDENT CHANGES IN NETWORK SHORT TERM PLASTICITY: the brainnetwork plasticity is known to change with age. Moreover, progression ofdegenerative disorders as AD, correlate with decrease in brain networkplasticity. According to some embodiments, DELPHI's analysis of thehistory dependency is configured to identify two physiologicalparameters of network functionality that best distinguishes betweengroups (healthy aging vs. MCI/mild dementia). According to someembodiments, the ratio between the total charge transfer of response (Q)evoked to an inhibitory protocol of stimulation (STP-Q), as demonstratedin FIG. 24A, and the ratio between the late slope component of evokedresponse to an inhibitory protocol of stimulation (STP-slope N100-P180),as demonstrated in FIG. 24B. According to some embodiments, asignificant age dependent increases in the charge transfer STP and adecrease in the late slope STP, as demonstrated in FIGS. 24A and 24B(data from the right hemisphere (contralateral to stimulation) parietalcortex is displayed).

According to some embodiments, the comparison of the two age comparablegroups of healthy elderly and patients with MCI/mild dementia,demonstrates a significant difference in both parameters. According tosome embodiments, the most significant change in STP between the twogroups is observed in the STP of the late slope, in which the MCI/milddementia group displays positive values (FIG. 24B) reflecting asignificant change in inhibitory response (P<0.001).

According to some embodiments, the ratio between these STP calculatedparameters demonstrates lower age dependency. According to someembodiments, these extracted cortical plasticity ratio values can bedisplayed as individual pseudo-color-coded images. FIG. 24C-24F,demonstrate the significant differentiation between normal, healthyaging and early stages of dementia, implicating that while single pulseanalysis of evoked response demonstrates strong correlation with normalaging (as demonstrated in FIGS. 23A-23F), plasticity measures seem toprovide a robust parameter for separating normal fromabnormal-pathological aging, as in the current case of early stages ofdementia (as demonstrated in FIGS. 24A-24F).

FIG. 25 demonstrate correlation between brain atrophy of grey matter asmeasured by MRI in healthy subjects with parameters of network shortterm plasticity and connectivity, according to some embodiments of theinvention; and

FIG. 26 demonstrate prediction model for brain atrophy of grey matter asmeasured by MRI in healthy subjects by parameters of network short termplasticity and connectivity, according to some embodiments of theinvention.

CONCLUSION

The human brain is a highly complexed network, comprised of physical andlogical topology. Physical topology defines how nodes in a network arephysically linked and includes aspects such as location and physicaldistance between nodes. Logical topology describes the network hierarchyand data flow, or how signals behave in the network (D. S. Bassett etal., Efficient physical embedding of topologically complex informationprocessing networks in brains and computer circuits. PLoS Comput Biol 6,e1000748 (2010). V. M. Eguíluz, D. R. Chialvo, G. A. Cecchi, M. Baliki,A. V. Apkarian, Scale-free brain functional networks. Phys Rev Lett 94,018102 (2005)). In neuronal network context, physical topology refers tothe location and morphology of different brain regions, while thelogical topology refers to the nature of communication within andbetween the different brain regions. While every structural-physicalchange in brain topology is always manifested as functional-logicalchange, the opposite does not always stand. Thus, it is highly importantto monitor brain network logical topology in order to evaluate brainstatus and “health” throughout life, in the developing brain, duringaging, in the progression of neurodegenerative disorders andpathological deterioration.

Current study results display the ability of DELPHI using TMS-EEGtechnology for measuring crucial brain network parameters ofconnectivity and plasticity and its relevance for monitoring brainhealth. Network connectivity measures displayed in this study, indicatemonitorable changes that occur with age and point to the ability of thistechnology to monitor subtle structural and functional changes, as wellas the ability to differentiate normal and abnormal aging. Connectivitymaps display changes in connectivity between healthy and mild dementiasubjects mainly relating frontal areas, indicating a decrease ininter-hemispheric synchronicity, as well as decreased synchronicitybetween frontal and temporal or parietal areas (FIGS. 22A-22D).

These results are consistent with several structural and functionalstudies demonstrating intercortical disconnect such as changes in thecorpus callosum (CC) in early stages of AD and MCI (Di Paola et al.,2010a; Di Paola et al., 2010b; Frederiksen et al., 2011). Changes intranscallosal connectivity have also been displayed using TMS in a studydifferentiating between demented and cognitively impaired non dementedpatients (Lanza et al., 2013). TEP slopes, which provide a descriptionof TEP form and an excitation/inhibition reference (Rossi et al., 2009;Tremblay et al., 2019), display an age dependent decrease in both earlyand late slopes of response (FIGS. 23A-23B). This decrease may beassociated with atrophy of gray and white matter or changes inexcitation/inhibition balance as supported by anatomical MRI and EEGstudies which indicate reduced fiber tracks in frontal and temporalareas and front-occipital reduced synchronicity (Dipasquale andCercignani, 2016; Sexton et al., 2011; Teipel et al., 2016). Inaddition, TEP slopes display a clear separation of pathological milddementia group from healthy control which includes a phase shiftrepresented by slope changes, these may be accounted by severe brainatrophy and/or excitation/inhibition balance shift. Short termplasticity measures (FIGS. 24A-24B), which evaluates the changes inexcitation/inhibition balance, are shown to provide discrete parameterwhich display a sort of binary step function for differentiating thehealthy and diseased brain. These results may indicate as to the natureof significant changes in pathological population that results fromshifting in excitation/inhibition mechanisms as opposed to connectivityand structural changes that may account for age related changesdisplayed here. This study results support the significance and value ofTMS in understanding and monitoring brain health and pathological agingincluding neurodegenerative disorders such as Alzheimer's disease (AD)and vascular dementia. Studies of connectivity, excitability andplasticity utilizing TMS have provided evidence suggesting corticalexcitability changes in the early stages of the disease, as well asaltered cortical inhibition and cholinergic mechanisms (Bella et al.,2016; Bella et al., 2013; Ferreri et al., 2017; Lanza et al., 2017; Niand Chen, 2015). It has also been shown that TMS-EEG evoked potentials(TEP) poses major advantages as: (a) high reproducibility of evokedresponse within individuals over occipital, parietal, premotor, motorand prefrontal regions (Casarotto et al., 2010; Kerwin et al., 2018;Lioumis et al., 2009). (b) ability to measure TEP at sub MT intensities.Stimulating the M1 at intensities as low as 40% of the MEP threshold,exemplifying the sensitivity of the measure (Komssi and Kähkönen, 2006;Komssi et al., 2004). (c) Recorded both locally, and in distalelectrodes, allowing for the study of the spreading of activation overcortical areas (Ilmoniemi et al., 1997; Komssi et al., 2002).

As demonstrated above, using DELPhi allows to characterize functionalproperties of normal-healthy brains in a reproducible way. Moreoverbeing reliable, DELPhi shows high sensitivity to changes in brainfunctionality, related with age or pathology. These finding have a majorsignificant clinical importance as the new tool presented here providesclinicians not only with the ability to determine whether an individualbrain is healthy to his age group, in the evaluated physiologicalparameters, but also with the potential ability to detectpathological-degenerative process at a very early stage, with regards toany current available clinical tool. In addition, DELPhi has a hugepotential as a monitoring tool, evaluating brain directed drug effectson the individual brain network functionality. This understanding ishighly relevant to the psychiatric clinical practice, determining theoptimal course of pharmacological treatment for a patient, as well asfor monitoring the efficacy of rehabilitation process post stroke ortraumatic brain injuries.

To make the DELPhi technology accessible to clinicians, the numeric datamatrix collected during the evaluation procedure can be presented as acolor coded map brain image displaying absolute and relative to healthyquantitative measures of network's connectivity and plasticity. This newexpression of brain functional status completes current existingtechnologies as MRI and CT scan, which provide only limitedunderstanding focused on anatomical or perfusion changes but do notprovided the fundamentally needed direct understanding of brainfunctionality.

The clinical work presented herein describe the physiologicalcharacterization of brain functionality by stimulating a single corticallocation (left M1). It is important to note that simulation in multiplesites will most probably enable better mapping of brain functionalitymoreover in pathological conditions and brain insults as traumatic braininjuries and stroke, allowing early evaluation of the severity of damageand assessment of rehabilitation potential.

The extent of functional changes during brain aging varies amongindividuals in a way that cannot be quantified using current availableclinical tools. Early identification of abnormal brain aging isextensively researched, scanning genetic, biochemical, andneuropsychological aspects of the transition from normal to pathologicaging. The development of drug therapies or behavioral modifications toslow or possibly halt the complex processes involved in abnormal agingrequires the earliest possible intervention. Hence researchers aresearching for biochemical or imaging markers that might be used topredict the clinical course of early phases of dementia versus normalaging patterns or to monitor treatment progress.

The provided results demonstrate that evaluating electro-physiologicalproperties, as network connectivity, strength and plasticity, enable thecharacterization of age dependent brain functional changes and themonitoring of abnormal aging processes. It also presents the ability ofthe system in tracking changes in brain structure of grey matter andwhite matter that are known to affect connectivity and plasticity. Asdesaturated, single pulse connectivity parameters demonstrate an agedependent behavior that is significantly changed during abnormal aging,brain atrophy and structure, these parameters are supported by networkshort term plasticity data, shown to be significantly different inpathological aging. The data presented herein supports the understandingthat direct electrophysiological imaging is clinically efficient inevaluating brain functionality and provides a clinical tool formonitoring brain network function and brain health. The currentdemonstrations show that the DELPHI automated acquisition and analysissystem can be used in order to monitor brain health throughout aging andmay enable early detection of abnormal physiological changes leading toneurodegeneration, and evaluate brain structural changes.

While certain features of the invention have been illustrated anddescribed herein, many modifications, substitutions, changes, andequivalents will now occur to those of ordinary skill in the art. It is,therefore, to be understood that the appended claims are intended tocover all such modifications and changes as fall within the true spiritof the invention.

1. A computer implemented method for classifying a brain status of asubject, from a neural activity response of the subject to an inducedTMS stimulation; the method comprising: constructing a machine learningclassifier (MLC) configured to classify a subject's brain status;training the MLC using a training set, the training set comprising pairsof training output-classification vectors and their correspondingtraining input vectors, all extracted from a database of subjects withknown brain status classifications, wherein: each trainingoutput-classification vector is determined based on at least one of adatabase-subject's known: MRI readings, physician/s classification,cognitive test/s evaluation, and any combination thereof; each traininginput vector comprises features extracted from a database-subject'sbrain neural activity response to the induced TMS stimulation; applyingthe trained MLC on an input vector comprising features extracted from atested-subject's brain neural activity response to the induced TMSstimulation, to obtain an output classification vector for thetested-subject's brain status; wherein the TMS stimulation frequency isat least one selected from: below 0.5 Hz, for a neural response whichdoes not depend upon stimulations history; and above 0.5 Hz, for aneural response that is affected by previously provided stimulationspulses.
 2. The method of claim 1, wherein each output classificationvector and accordingly each training output classification vectorcomprise features selected from: physical status selected from:healthy/not healthy, the brain's evaluated age, neurological disorders,neurodegenerative disorders, Alzheimer, Dementia, small vessels disease,Psychiatric disorders, depression, chronic pain, physical injury,pathophysiological abnormalities, structural damage of the grey matter,structural damage of the white matter, functional damage, internalbleeding, balance between excitation and inhibition in the regionalcortical network, intra-cranial pressure, cerebro-vascular accident(CVA), Basal ganglia injury, brain stem injury, corticospinal trackinjury, frontal lobe injury, temporal lobe injury, diabetes,hypertension, any combination thereof; brain MRI-T1—gray matter andwhite matter volume and/or surface of cortical and subcortical areas;diffused tensor MRI imaging (MRI-DWI)—white matter measures offractional anisotropy (FA) and mean diffusivity (MD); and anycombination thereof.
 3. The method of claim 2, further comprising a stepof determining each training output-classification vector, based on atleast one of a database-subject's known features.
 4. The method of claim1, wherein the MLC comprises at least one module selected from: a multilayered MLC; a classification module, configured for separation of theextracted features into discrete classification groups, selected from:support vector machine (SVM), decision trees, and K-nearest neighbors; aregistration module configured for continuous data prediction, selectedfrom: linear and/or non-linear regression, artificial neural network(NN), and adaptive fuzzy logic learning; and any combination thereof. 5.The method of claim 1, wherein each of the input vectors and accordinglyeach of the training input vectors further comprises at least onefeature selected from: age, gender, known medical status, drugtreatment, blood pressure, and any combination thereof.
 6. The method ofclaim 1, wherein the TMS simulation frequencies are: at least oneselected from below 0.5 Hz, for a neural response, which does not dependupon stimulations history; and at least one selected from above 0.5 Hz,such that a neural response to pulses are affected by the previouslyprovided pulses, thereby indicating short term plasticity.
 7. The methodof claim 1, further comprising steps of: receiving, via an EEG device, aneural activity response of a subject's brain to the induced TMSstimulation to one or more predetermined brain regions of a subject; andextracting response features from the subject's neural activityresponse, as elements for an input vectors or a training input vector.8. The method of claim 7, wherein the step of extracting is at leastbased on positive and negative peaks at the neural activity response,and wherein the response features comprise at least one of theresponse's: signal amplitudes; amplitude latencies; principle componentanalysis (PCA) and/or independent component analysis (ICA); slopesbetween positive and negative peaks; charge transfer; lag of signalphase from healthy signal; signal correlation to a healthy subjectsignal model; ratio between segments in the signal of the same sensor,when TMS induced frequency is above 0.5 Hz; coherence between the signalof different sensors; brain connectivity; and any combination thereof.9. The method of claim 8, wherein the step of extracting comprisesdetermining the positive and negative peaks, at time steps selected froma group consisting of: about 60 mSec, about 100 mSec, about 180 mSec andany combination thereof.
 10. The method of claim 8, wherein the step ofextracting comprises determining the positive and negative peaks, attime steps selected from a group consisting of: about 45 mSec, about 120mSec, about 180 mSec, about 360 mSec and any combination thereof. 11.The method of claim 8, wherein the extracted slopes are provided betweendetermined positive and negative signal peaks, which are adjacent,thereby extracting peaks' relation.
 12. The method of claim 9, whereinthe step of extracting features further comprises comparing the slope ofthe 60 mSec peak with the 100 mSec peak (60-100 slope), versus the slopeof 100 mSec peak with 180 mSec peak (100-180 slope).
 13. The method ofclaim 10, wherein the step of extracting features further comprisescomparing the slope of the 45 mSec peak with the 120 mSec peak (45-120slope), versus the slope of 180 mSec peak with 300 mSec peak (180-300slope).
 14. The method of claim 1, wherein the TMS is induced in severalsequential stimulations, each at different intensity.
 15. The method ofclaim 1, wherein the TMS stimulated brain region is selected from agroup consisting: frontal, parietal, temporal, occipital (right and lefthemispheres) and any combination thereof.
 16. An apparatus configured toevaluate brain state of a subject, the apparatus comprising: a directedinspective/diagnostic stimulation unit, configured to induce TMSdiagnostic stimulation to a predetermined brain region of the subject; abrain activity EEG sensor, configured to measure a neural activityresponse to the diagnostic stimulation induced by the directed brainstimulation unit; and a processing circuitry and at least one memoryunit, in wired or wireless communication with the brain activity sensor,the processing circuitry is configured to execute at least the steps ofa computer implemented method for classifying a brain status of asubject, from a neural activity response of the subject to an inducedTMS stimulation; wherein the method steps are configured to: construct amachine learning classifier (MLC) configured to classify a subject'sbrain status; train the MLC using a training set, the training setcomprising pairs of training output-classification vectors and theircorresponding training input vectors, all extracted from a database ofsubjects with known brain status classifications, wherein: each trainingoutput-classification vector is determined based on at least one of adatabase-subject's known: MRI readings, physician/s classification,cognitive test/s evaluation, and any combination thereof; each traininginput vector comprises features extracted from a database-subject'sbrain neural activity response to the induced TMS stimulation; apply thetrained MLC on an input vector comprising features extracted from atested-subject's brain neural activity response to the induced TMSstimulation, to obtain an output classification vector for thetested-subject's brain status; and wherein the TMS stimulation frequencyis at least one selected from: below 0.5 Hz, for a neural response whichdoes not depend upon stimulations history; and above 0.5 Hz, for aneural response that is affected by previously provided stimulationspulses.
 17. A non-transitory computer readable medium (CRM) that, whenloaded into a memory of a computing device and executed by at least oneprocessor of the computing device, configured to execute at least thesteps of a computer implemented method for classifying a brain status ofa subject, from a neural activity response of the subject to an inducedTMS stimulation; wherein the method steps are configured to: construct amachine learning classifier (MLC) configured to classify a subject'sbrain status; train the MLC using a training set, the training setcomprising pairs of training output-classification vectors and theircorresponding training input vectors, all extracted from a database ofsubjects with known brain status classifications, wherein: each trainingoutput-classification vector is determined based on at least one of adatabase-subject's known: MRI readings, physician/s classification,cognitive test/s evaluation, and any combination thereof; each traininginput vector comprises features extracted from a database-subject'sbrain neural activity response to the induced TMS stimulation; apply thetrained MLC on an input vector comprising features extracted from atested-subject's brain neural activity response to the induced TMSstimulation, to obtain an output classification vector for thetested-subject's brain status; and wherein the TMS stimulation frequencyis at least one selected from: below 0.5 Hz, for a neural response whichdoes not depend upon stimulations history; and above 0.5 Hz, for aneural response that is affected by previously provided stimulationspulses.